Evidence map›Paper›PMID 24878498›Full record

ArticleMolecular & cellular proteomics : MCP2014

PepExplorer: a similarity-driven tool for analyzing de novo sequencing results.

Felipe V Leprevost, Richard H Valente, Diogo B Lima, Jonas Perales, Rafael Melani, John R Yates, Valmir C Barbosa, Magno Junqueira, Paulo C Carvalho

Abstract read
In one paragraph

Article in Molecular & cellular proteomics : MCP, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Felipe V LeprevostFrom the ‡Laboratory for Proteomics and Protein Engineering, Carlos Chagas Institute, Fiocruz, Paraná, Brazil;
Richard H Valente§Laboratory of Toxinology, Oswaldo Cruz Institute, Fiocruz, Rio de Janeiro, Brazil; ¶Instituto Nacional de Ciência e Tecnologia em Toxinas (INCTTox/CNPq), Brazil;
Diogo B LimaFrom the ‡Laboratory for Proteomics and Protein Engineering, Carlos Chagas Institute, Fiocruz, Paraná, Brazil;
Jonas Perales§Laboratory of Toxinology, Oswaldo Cruz Institute, Fiocruz, Rio de Janeiro, Brazil; ¶Instituto Nacional de Ciência e Tecnologia em Toxinas (INCTTox/CNPq), Brazil;
Rafael Melani‖Proteomics Unit, Rio de Janeiro Proteomics Network, Department of Biochemistry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil;
John R Yates**Department of Chemical Physiology, The Scripps Research Institute, La Jolla, California;
Valmir C Barbosa‡‡Systems Engineering and Computer Science Program, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
Magno Junqueira‖Proteomics Unit, Rio de Janeiro Proteomics Network, Department of Biochemistry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil;
Paulo C CarvalhoFrom the ‡Laboratory for Proteomics and Protein Engineering, Carlos Chagas Institute, Fiocruz, Paraná, Brazil;

Funding

TrainingP41GM103533 · NIGMS · UNIVERSITY OF WASHINGTON · PI MACCOSS, MICHAEL · 2012 to 2021
$20.7M
Quantitative Proteome Analysis of Brain DevelopmentR01MH067880 · NIMH · SCRIPPS RESEARCH INSTITUTE, THE · PI YATES III, JOHN R · 2003 to 2017
$7.1M
NIGMS NIH HHS P41 GM103533NIMH NIH HHS R01 MH067880
6 · The paper itself

Abstract

Peptide spectrum matching is the current gold standard for protein identification via mass-spectrometry-based proteomics. Peptide spectrum matching compares experimental mass spectra against theoretical spectra generated from a protein sequence database to perform identification, but protein sequences not present in a database cannot be identified unless their sequences are in part conserved. The alternative approach, de novo sequencing, can make it possible to infer a peptide sequence directly from a mass spectrum, but interpreting long lists of peptide sequences resulting from large-scale experiments is not trivial. With this as motivation, PepExplorer was developed to use rigorous pattern recognition to assemble a list of homologue proteins using de novo sequencing data coupled to sequence alignment to allow biological interpretation of the data. PepExplorer can read the output of various widely adopted de novo sequencing tools and converge to a list of proteins with a global false-discovery rate. To this end, it employs a radial basis function neural network that considers precursor charge states, de novo sequencing scores, peptide lengths, and alignment scores to select similar protein candidates, from a target-decoy database, usually obtained from phylogenetically related species. Alignments are performed using a modified Smith-Waterman algorithm tailored for the task at hand. We verified the effectiveness of our approach using a reference set of identifications generated by ProLuCID when searching for Pyrococcus furiosus mass spectra on the corresponding NCBI RefSeq database. We then modified the sequence database by swapping amino acids until ProLuCID was no longer capable of identifying any proteins. By searching the mass spectra using PepExplorer on the modified database, we were able to recover most of the identifications at a 1% false-discovery rate. Finally, we employed PepExplorer to disclose a comprehensive proteomic assessment of the Bothrops jararaca plasma, a known biological source of natural inhibitors of snake toxins. PepExplorer is integrated into the PatternLab for Proteomics environment, which makes available various tools for downstream data analysis, including resources for quantitative and differential proteomics.

Indexed as

AlgorithmsDatabases, ProteinAmino Acid SequenceAnimalsArchaeal ProteinsBothropsMass SpectrometryPlasmaProteomicsPyrococcus furiosusSequence AlignmentSequence Analysis, ProteinArchaeal Proteins

Identifiers

PMID24878498
PMCPMC4159663

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.